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질문 1 의 8

Which interpretability difficulty motivates sparse dictionary learning for neural activations?

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  1. Which interpretability difficulty motivates sparse dictionary learning for neural activations?
    • Every model activation is already a complete dictionary definition
    • Several learned signals can overlap in an activation space
    • The tokenizer must be permanently removed
    • All neural weights are stored as image pixels
  2. What does an SAE sparsity constraint encourage for a given input?
    • Every feature to activate equally on every input
    • A mandatory removal of its decoder
    • A guarantee that every feature has exactly one perfect human label
    • Only a limited subset of learned feature activations to be nonzero
  3. Anthropic의 'Scaling Monosemanticity' 작업에서 유명한 예시 기능은 무엇이었나요?
    • 금문교의 특징
    • 숫자 7의 특징
    • 파란색에만 해당되는 기능
    • 모음의 특징
  4. What does an overcomplete SAE dictionary mean?
    • It must activate every feature for every token
    • It contains no reconstruction mapping
    • It has more learned feature directions than input activation dimensions
    • It always requires less storage than the input vector
  5. What does a monosemantic feature label aim to describe in this research context?
    • A guarantee that the feature never responds to related examples
    • A feature associated with one interpretable concept
    • A language model that has only one parameter
    • A token represented by exactly one raw byte
  6. 모델의 동작에 영향을 주기 위해 SAE를 어떻게 사용할 수 있습니까?
    • 출력을 조정하기 위해 기능을 켜거나 끄는 방법
    • 전체 모델을 처음부터 다시 학습하여
    • 토크나이저를 삭제하여
    • 이미지 모델로 변환하여
  7. What does an SAE’s reconstruction residual measure for an activation vector?
    • The difference between two unrelated document titles
    • The total number of words in the model vocabulary
    • Only the model’s hardware temperature
    • The difference between the original activation and its decoded reconstruction
  8. When an SAE is fitted to collected activations of a frozen language model, which weights need to be trained for that fitting?
    • Every original language-model weight must be updated
    • Only the tokenizer’s character list
    • The SAE’s own encoder and decoder weights
    • No parameters of the SAE can change